Method and system for generating channel codes, in particular for a frame-header

ABSTRACT

A method for generating a channel code, in particular for a frame-header, wherein at least a code-word of the channel code is obtained by means of at least a concatenation of code-words of two constituent codes and such concatenation is performed on subsets of code-words of a first constituent code, having maximum length, with code-words of a second constituent code.

FIELD OF THE INVENTION

The present invention relates to a method and a system for generatingchannel codes, in particular channel codes for a frame-header in an ACM(“Adaptive Coding and Modulation”) communication system.

DESCRIPTION OF THE PRIOR ART

Real-time adaptation of transmission parameters according to the channelconditions is highly desirable feature in the communication systemswhere the channel parameters may change in time or from one receiver toanother.

Time and/or user varying channel condition is an importantcharacteristic of most communication systems such as satellite, networkand broadcast systems.

According to the prior art, adaptive coding and modulation schemes havebeen proposed in such systems to provide significant capacity gains byreal-time adaptation of the FEC (“Forward Error Correction”) coderate/length and modulation constellation.

The main idea of an ACM scheme is to increase the capacity of a systemcommunication by avoiding the waste of resources caused by adopting afixed physical layer scenario by which the spectral efficiency must besacrificed to guarantee a good performance for the user with the worstchannel conditions.

By employing an ACM scheme, the transmitter is able to switch betweenseveral constellations and codes choosing the largest availablemodulation and code rate which ensures a target DER (“Detection ErrorRate”), therefore insuring maximum spectral efficiency for each user. Atthe receiver, to successfully decode the message, each user should beable to decide whether the message is intended to him and/or torecognize the parameters of the constellation and code which has beenused by the transmitter.

In general, each packet sent by the transmitter consists of two mainparts.

The first part is called frame-header, or simply header, and containsthe informations regarding the modulation and coding, hereinafter calledACMI (“Adaptive Coding and Modulation Indicator”).

The second part contains the message which is encoded using thecorresponding ACMI parameters.

Therefore each user should first decode the information in theframe-header in order to be able to decode the rest of the message.

In applications such as satellite broadcasting, the coding strategy isnot trivial due to the wide dynamic range of the SNR (“Signal to NoiseRatio”), typically from −15 dB to 15 dB. Only the use of an ACM schememay not be sufficient to ensure the radio link, a good spectralefficiency and a target probability of error to the receiver.

It is therefore important to improve the spectral efficiency through ageneration of a code which allows to minimize the average and maximumlength of a frame-header.

In the patent application no. US 2010/0128661, the code generationprocedure is based on heuristic considerations without any attempt tominimize the frame-header length. The variable length code consists inthe repetition of a single strong Reed-Muller code to cope with thedifferent signal to noise ratio conditions.

However, such patent application does not teach to minimize a length ofa frame-header. It is the main object of the present invention toindicate a method and a system for generating channel codes, inparticular for a frame header, able to minimize an average and a maximallength of a frame-header of a data packet.

It is a further object of the present invention to indicate a method anda system for generating channel codes, in particular for a frame header,able to maximize the spectral efficiency of a communication system.

It is a further object of the present invention to indicate a method anda system for generating channel codes, in particular for a frame header,able to improve the correction capability of the generated channel code.

It is a further object of the present invention to indicate a method anda system for generating channel codes, in particular for a frame header,able to decrease the computational complexity of a decoder.

It is a further object of the present invention to indicate a method anda system for generating channel codes, in particular for a frame header,able to obtain a fixed length for all code-words in order to have a samecomplexity of the maximum likelihood decoder of a variable length code.

These and other objects of the invention are achieved by a method and asystem for generating channel codes, in particular for a frame header,as claimed in the appended claims, which are intended to be an integralpart of the present description.

DETAILED DESCRIPTION

In short, it is disclosed a method for generating a channel code, inparticular for a frame-header, wherein at least a code-word of saidchannel code is obtained by means of at least a concatenation ofcode-words of two constituent codes and such concatenation is performedon subsets of code-words of a first constituent code, having maximumlength, with code-words of a second constituent code.

The users are divided, for example, into a smaller set of M typesaccording to their radio link quality. Each user type i is associatedwith an available signal to noise ratio SNR_(i). It is assumed that forany two user types k and m, if k>m, then SNR_(k)>SNR_(m). A set ofmaximum ACM modes, ACM_(i) are associated to the set of user types. Eachuser type i must be capable of detecting and decoding all packetsencoded with ACM modes j≦i, corresponding to signal to noise ratio SNRlower or equal to that associated to the user type i. Consequently,users of higher types, corresponding to high SNR values, must then beable to detect the ACM mode within a larger set than that of users oflower type.

This corresponds to the generation of a code which admits a sequence ofsubsets of code-words of such code with very large differences in thecorrection capabilities.

Further features of the invention are set out in the appended claims,which are intended to be an integral part of the present description.

The above objects will become more apparent from the following detaileddescription of a method for generating channel codes, with particularreference to the annexed drawings, wherein:

FIG. 1 shows a block diagram of a communication system;

FIGS. 2 and 3 show modes of operation of an ACMI detector for a usertype i;

FIG. 4 shows an example of the code generation using a variable lengthcoding;

FIG. 5 shows parameters of codes for a variable length code withthirty-two modes using the method according to the present invention andthe Hamming code bound with DER (“Detection Error Rate”) equal to 10⁻⁶;

FIG. 6 shows parameters of codes for a variable length code withthirty-two modes using linear codes of the prior art with DER equal to10⁻⁶;

FIG. 7 shows parameters of codes for a variable length code withforty-eight modes using linear optimal codes of the prior art with DERequal to 10⁻⁸;

FIGS. 8 and 9 show parameters of codes for a variable length code usingthe method according to the present invention and linear optimal codesof the prior art with DER equal to 10⁻⁸ with respectively thirty-two andforty-eight modes;

FIG. 10 shows a graph relating to data of FIG. 8;

FIGS. 11 a e 11 b show code-words of the code of FIG. 8;

FIGS. 12 and 13 show respectively required header packet length versusminimal signal to noise ratio SNR for some values of k bits when DER isfixed to 10⁻⁶ and data relating to FIG. 12, thus corresponding to astraightforward worst case design of frame-header;

FIGS. 14 and 15 show respectively required header packet length versusminimal signal to noise ratio SNR for some values of k bits when DER isfixed to 10⁻⁸ and data relating to FIG. 14, thus corresponding to astraightforward worst case design of frame-header.

With reference to FIG. 1, it is shown a communication system 10 used asan example of system scenario to explain the detailed description of thepresent invention. A plurality of users is served by a gateway 1 througha satellite transponder 2 and the gateway 1 comprises at least oneencoder 3 that generate at least a code.

The users are divided into M types, for example M≦64, according to theircurrent radio link quality and this subdivision is created by a designerduring the design of the communication system 10.

Each user type i is associated with an available signal to noise ratioSNR_(i) and it is assumed, with no loss of generality, thatSNR_(i)≦SNR_(i+1)≦ . . . ≦SNR_(M). This latter condition is typical ofsatellite communication systems. Thus, signal to noise ratio isdifferent for each user type i. Furthermore, each user type i comprisesat least one physical user.

A set of maximum ACM modes are associated to each user type i. Each usertype i must be capable of detecting and decoding all packets encodedwith modes ACM_(j) with j≦i. A feedback channel, not represented in FIG.1, allows the gateway 1 to know the type i of each user.

Furthermore, the gateway 1 has the possibility of choosing for each usertype i an ACM mode with index j≦i. Choosing a value below the maximumallowed i, which in principle is suboptimal for the total systemcapacity, allows some flexibility at the gateway 1. This additionalsystem flexibility, however, requires that user type i detects anddecodes all modes ACM_(j) with j≦i. This requirement then imposes a highdetection complexity especially to users associated to high SNR values.In particular, users with the highest user type M must detect and decodeall packets from a satellite. The filtering of the packets, according tothe true intended destination, is deferred after the decoding process.

With reference to FIG. 2, it is shown a mode of operation of a receiver23 comprising an ACMI detector 21 for a user type i, and an input signaly, in the communication system 10 described in FIG. 1. The ACMI detector21 delivers an index ĵ in the range [1, . . . , i] denoting anestimation of an ACM index in the allowed range, or the conventionalsymbol “0” to denote a failure in the detection, freezing a followingdecoder 22. In this example of communication system 10, the followingtypes of events for an ACMI are:

-   -   Detection error: P_(E)=P(ĵ≠j|j≦i)

This refers to the probability that ACM/_(J), estimated from the ACMIdetector 21, is transmitted to the decoder 22 from the ACMI detector 21and it is not equal to the ACMI_(j) received from gateway 1, given thatindex j is less or equal to the index i of the user type. A packet thatis potentially intended to the user type i is decoded with an uncorrectACM mode and consequently not correctly delivered.

-   -   Useless decoding: P_(U)=P(ĵ≠0|j>i)

This refers to the probability that ACMI_(j), estimated from ACMIdetector 21, is transmitted to the decoder 22 from the ACMI detector 21and it is not equal to zero, given that index j is greater than theindex i of the user type. A packet that is not intended to users of typej is incorrectly decoded with the wrong format. The only cost is auseless decoding.

From that aforementioned, it is important to design and generate codesfor minimizing the probability of the detection error P_(E), given thatthe cost of useless decoding P_(U) is marginal.

With reference to FIG. 3, it is worthwhile to notice that by removingthe flexibility for the gateway 1 of choosing a mode j≦i, and thusimposing j=i at the gateway 1, the only function of an ACMI detector 31is to activate or to freeze a following decoder 32.

An error of the ACMI detector 31 would only cause a useless decodingevent. The number of useless decoding in this case can be drasticallyreduced also for users with good link quality.

In a first example of the present invention it is described a method forgenerating a channel code for minimizing the aforementioned detectionerror probability P_(E), at which is associated a DER (“Detection ErrorRate”) of ε value, and with variable length code-words.

The set of code-words is generated with variable lengths according tothe following algorithm, which is shown in FIG. 4:

1. Fixing a set of M ordered SNR values SNR_(i) corresponding to thedesired modes thresholds and a desired DER ε;2. Setting a total distance d_(T)=0;3. For all i decreasing from M to 2;4. Computing the required incremental distance to achieve the desiredDER ε at target SNR_(i)

d _(i) =d _(min)(ε,SNR _(i) ,i)−d _(T)  (1)

The minimum distance d_(min) or d_(i) depends on a signal to noise ratioSNR and on a detection error rate with ε value. Furthermore, the minimumdistance d_(i) increases for each generated subset of code and it iscomputed for each user type i.5. If d_(i)=0 repeat from step 3.6. else generating a code with code-words having minimized length n_(i),i code-words and minimum distance d_(i)7. Setting the total distance d_(T)=d_(T)+d_(i)8. Repeat from step 3.9. The set of variable length code-words is finally obtained byconcatenating a code-word from each of the constituent codes (see FIG.4). The code-word of the mode i is then obtained by concatenatingcode-words of constituent codes from M down to i and have total lengthN_(i) as follows:

$\begin{matrix}{N_{i} = {\sum\limits_{m = 1}^{M}n_{m}}} & (2)\end{matrix}$

With reference to FIG. 4, at least a code-word of constituent codes isencapsulated into a code-word of the channel code and at least acode-word of the channel code has length greater than remainingcode-words of such channel code. Furthermore, the concatenation isperformed on subsets of code-words of a first constituent code, havingmaximum length, with code-words of a second constituent code.

In the formula (1) it is defined the following function:

$\begin{matrix}{{d_{\min}\left( {\varepsilon,{S\; N\; R},M} \right)} = \frac{{Q^{- 1}\left( {\varepsilon \text{/}\left( {M - 1} \right)} \right)}^{2}}{{2 \cdot S}\; N\; R}} & (3)\end{matrix}$

where Q is the Q-function, i.e. the complementary cumulativedistribution function of the random variable standards.

The formula (3) provides the required minimum distance d_(min) for achannel code with M code-words achieving a detection error rate ε at thesignal to noise ratio SNR. This formula (3) is obtained by using theupper bound:

ε≦(M−1)Q√{square root over (2d _(min) ·SNR))}  (4)

which is rather accurate for small M and almost perfect codes.

In order to have a lower bound on what can be achieved is used, forexample, the Hamming bound

$\begin{matrix}{M \leq \frac{2^{n}}{\sum_{i = 0}^{t}\begin{pmatrix}n \\i\end{pmatrix}}} & (5) \\{t = \left\lfloor \frac{d_{\min} - 1}{2} \right\rfloor} & (6)\end{matrix}$

where t is the maximum weight of error vectors that are surely correctfrom the minimum distance decoding, to find the minimal length n of acode with M code-words and minimum distance d_(min) required in step 6of the previous algorithm.

With reference to FIG. 5, it is shown a result of a channel codegeneration using the previous algorithm for a system with 32 modes,signal to noise ratio SNR ranging from +15 to −16 dB in steps of 1 dB,for a target DER ε=10⁻⁶.

The columns of the table of FIG. 5 indicate, from left to right, thenumber of code-words i, that is also the index of the user type, thecorresponding signal to noise ratio SNR_(i), the incremental length ofcode-word n_(i) of an header with index i, the number of bits k_(i) ofcode-word of an header at index i, the incremental minimum distance ofcode-word d_(i) of an header at index i, the total minimum distanced_(T) i.e. the sum, at index i, of preceding d_(i), the total code-wordlength N_(i) of an header i.e. the sum, at index i, of preceding n_(i)and, in the last column, i·N_(i) that indicate the complexity of thedecoding.

It is to be noted that in this case the maximal code-word length N_(i)is 464 symbols, indicated with the reference 51, and the average headerlength E{N_(i)}, assuming that all modes have the same probability, isonly 119 symbols, indicated with the reference 52. Hamming bound is anupper bound to the number of code-words and there are few codes thatachieve it.

With reference to FIG. 6, it is reported a result of a channel codegeneration using optimal constituent linear codes noted in the priorart, taken from the tables found on M. Grassl, “Bounds on the minimumdistance of linear codes and quantum codes”, available online athttp://www.codetables.de, published in 2007.

These constituent linear codes, noted in the prior art, have theadditional constraint of having a number of code-words that is a powerof two, i.e. 2^(k), wherein k is the number of information bits. The useof these linear codes requires a maximal code-word length of 609symbols, indicated with the reference 61, and an average header lengthE{N_(i)} of only 149 symbols, indicated with the reference 62.

By comparing FIG. 5 and FIG. 6, it is therefore clear that the channelcode, generated using the previous algorithm according to the presentinvention, is able to minimize the average and the maximal length of aframe-header of a data packet.

With reference to FIG. 7, it is compared an example of the generatedcode length according to the present invention for 48 modes with therequired header length for the signaling scheme specified in D. Becker,N. Velayudhan, A. Loh, J. O'Neill, and V. Padmanabhan, “Efficientcontrol signaling over shared communication channels with wide dynamicrange”, described in the U.S. patent application Ser. No. 12/621,203.The DER is fixed into ε=10⁻⁸.

It can be noticed that the channel code generation proposed according tothe present invention allows to reduce considerably the header lengthfor all modes. The average of header length E{N_(i)} according to theU.S. patent application Ser. No. 12/621,203 is 750 (reference 71), whilethe average of the header length E{N_(i)} (reference 72) is reduced to301 according to this example of the present invention; in the same waythe maximum header length is reduced from 4416 (reference 73) to 1285(reference 74).

Therefore, using the method according to the present invention, byevaluating the average length E{N_(i)}, an improvement of about 60% isobtained.

The aforementioned algorithm, when applied to constituent linear codes,shows some weaknesses in the channel code length. This is mainly due tothe fact that the number of code-words of a constituent linear code isalways a power of 2.

In this case, for example, all modes from 32 to 17 in FIG. 6 share thesame size. In such cases, it is useful to generate a single constituentcode which guarantees a good minimum distance instead of using acompletely incremental approach as described above.

It is therefore proposed an example of modified code generationalgorithm as follows:

1. Fixing a set of M ordered SNR values SNR_(i) corresponding to thedesired modes thresholds and a desired DER ε.2. Setting d_(T,M+1)=0 and N_(T,M+1)=0.3. For all i decreasing from M to 24. Setting k=┌log₂ i┐5. Computing the required minimum distance

d _(T,i) =d _(min)(ε,SNR _(i) ,i)

6. For all i<j<2^(k) (modes with the same code dimension)7. Computing the incremental distance

δ_(i,j) =d _(T,i) −d _(T,j)

8. Generating a (n_(i,j), k, δ_(i,j)) linear code with minimal lengthn_(i,j).Notice that when the distance increment δ_(i,j) is zero, the code haslength 0. In this case, k is the number of information bits and not thenumber of code-words.9. Repeat from 6.10. Picking from the previous set of codes the one with the minimaltotal length corresponding to j*=arg min_(j)n_(i,j)+N_(T,j)11. Setting N_(T,j)=n_(T,j*) and d_(T,j)=d_(T,i), for all i≦j≦j*12. Repeat from 3.13. The set of variable length code-words is finally obtained byconcatenating code-words from each of the constituent codes (see FIG.4).

It can be noticed that, in this example, at each step are considered allthe possible constituent codes with the same dimension k guaranteeingthe minimum distance d_(T,i). This generation allows at steps i of theiteration to change the codes generated in previous steps j≧i, providedthat j<2^([log) ² ^(i]).

With reference again to the case of FIG. 4, at least a code-word ofconstituent codes is encapsulated into a code-word of the channel codeand at least a code-word of the channel code has length greater thanremaining code-words of such channel code. Furthermore, theconcatenation is performed on subsets of code-words of a firstconstituent code, having maximum length, with code-words of a secondconstituent code.

The result of this new generation of codes are reported in FIG. 8 andFIG. 9 and the latter is compared with FIG. 7. It is important to noticethat this new generation of codes allows to slightly reduce both theaverage and the maximum length of header.

In fact, the maximum length of header of FIG. 9 is 1239 symbols,indicated with the reference 91, which is lower than the maximum lengthof 1285 symbols of the header of FIG. 7, indicated with the reference74; while the average length of header E{N_(i)} is reduced from 301symbols, indicated with the reference 72, to 285 symbols, indicated withthe reference 92.

Furthermore, it should be noticed that the number of generated codes isreduced. An example of generated code is represented in FIG. 10 and inFIG. 11 a and in FIG. 11 b are shown relative code-words.

With reference to FIG. 10, there are reported the simulation resultsrelative to FIG. 8. The abscissa of FIG. 10 indicates the gap in dB fromthe nominal SNR_(i) of each user type i. In this case many codesactually achieve the target DER with a very large margin.

For example, the curve of user type 32, indicated in the FIG. 10 withthe reference number 101, achieves the target DER ε=10⁻⁶ with a marginof about 16.3 dB. This is due to the fact that the same code (39,5,19)is used for all types ranging from 32 to 17, and the target DER isachieved with no margin only for the user type 17, corresponding to anominal SNR of 0 dB.

It is also possible to evaluate the performances of the described aboveexamples, according to the present invention, in term of detection errorrate DER with respect to a worst case design.

The worst case design provides a coding example where all users are ableto decode all ACMI headers. A Reed-Muller code class is chosen asconstituent code and its parameters will be evaluated as a function ofthe required signal to noise ratio SNR range and number of modes M.

In the worst case design, the fact that each user type i can beassociated to a code with different code-word lengths or protection isnot exploited. The code design is such that each user type i is capableto retrieve the ACMI from the header. Remembering that the user type idecides to proceed with decoding only if the detected ACMI_(ĵ) is lessthan or equal to its type, i.e., ĵ≦i. In this case, the channel codegeneration is rather simple and strictly depends on the minimal signalto noise ratio SNR allowed in the system.

Taking the asymptotic expression of the DER ε for a code with minimumdistance d_(min), number of nearest neighbours A and repeating it Rtimes, is achieved:

$\begin{matrix}{\varepsilon = {\left. {{AQ}\left( \sqrt{2d_{\min}{R \cdot S}\; N\; R} \right)}\rightarrow{Rd}_{\min} \right. = \frac{{Q^{- 1}\left( {\varepsilon \text{/}A} \right)}^{2}}{{2 \cdot S}\; N\; R}}} & (7)\end{matrix}$

It is considered the class of Reed-Muller codes as a solution for worstcase design. The Reed-Muller codes have the parameters (n=2^(m), k=m+1),with d_(min)=2^(m-1) and A=2^(k)−2.

With reference to FIG. 12 and FIG. 13, there is reported the requiredheader length Rn to achieve a DER ε32 10⁻⁶ versus the minimal targetSNR. The different curves in FIG. 12 refer to the use of Reed-Mullercodes with k=1 to 8. The number of the needed repetitions can be easilycomputed given the length of the corresponding Reed-Muller. The curveindicated with k=6 is the one that should be considered for a systemwith 64 ACM modes. It can be noticed that, in order to design a channelcode working at −16 dB with 32 modes and DER ε=10⁶, a header length of1168 (reference 131) is required with a worst case design, while themaximum header length of the examples of variable length codingaccording to the present invention described above is 464 (reference 51)shown in FIG. 5, using Hamming bound, and 609 (reference 61) shown inFIG. 6, using in our construction linear codes of the prior art.

With reference to FIG. 14 and FIG. 15, there is reported the requiredheader length Rn achieved setting a DER ε=10⁻⁸. In this latter case, themaximum header length at −16 dB with 32 modes and DER ε=10⁻⁸ is 1520(reference 151), while the maximum header length of the examples ofvariable length coding of the present invention described above is 791(reference 81) shown in FIG. 8.

It is clear from this comparison that the header length decreases whenusing the method according to the present invention.

With reference to FIG. 10, there are shown simulation results using amaximum likelihood decoder, i.e. to maximize the probability P( y| c).It is done by calculating the correlation of a received signal y withall the code-words of c code and then choosing the code-word withlargest correlation. It is important to notice that the user type ineeds to calculate the correlation between only the first N, elements ofthe received signal y and only for the i code-words associated to SNR'swhich are smaller or equal to its nominal SNR (SNR_(i)).

Therefore, the decoding complexity is different for each user type i andis equal to N_(i)×i sums and i comparisons. The average decodingcomplexity can be calculated as E{N_(i)×i}. In FIGS. 5 to 9 the lastcolumn shows N_(i)×i results with their average values, assuming auniform distribution over all the users.

Since in the examples described above the linear constituent codes arechosen to be optimal for the given parameters and no other particularstructure is assumed, the best optimal decoding strategy is to computethe correlation of the received signal y (N_(i) sums) with all the icandidate code-words.

The complexity of this exhaustive decoder is affordable because it isdealing with small codes and short average lengths.

The complexity of the correlations is further reduced by usingconstituent codes which admit faster decoding algorithms, as theHadarmard transform used for Reed-Muller or maximal length codes. Inparticular, for a given set of parameters (n, k, d) where n and k aresufficiently small, repetition of maximal length codes yields codes withalmost optimal performances.

The main goal of UEP (“Unequal Error Protection”) is to design a codewhich offer a larger error protection to some bits, symbols orcode-words than others. In the literature the same name is used for allthree cases.

Hereinafter it is described a further example of the present invention,only addressing the code-word UEP, which uses a modification of thevariable length channel code generation described above to generate afixed length code.

The variable length code also has code-words with different errorprotection capabilities.

In fact, one way to generate a block UEP code is to extend all thecode-words of the code obtained for variable length codes by zero tohave a fixed length for all code-words (to equalize the length of allcode-words), thus at least a code-word comprises a sequence of bitshaving values equal to zero. In this case all code-words will have thelength equal to the longest code-word in the starting code.

It can be noticed that for this strategy to work, one should not use theall zero code-words in the construction of the starting variable lengthcode.

It is important to notice that in the example of variable length codingdescribed above it should be considered the average length as theeffective length of the code.

As for the complexity of the maximum likelihood correlator decoder forthe generated code by zero padding, it has in principle the samecomplexity as the corresponding variable length coding and therefore theaforementioned description about decoding complexity is valid also forthese codes.

The features of the present invention, as well as the advantagesthereof, are apparent from the above description.

A first advantage of the method for generating channel codes accordingto the present invention is that the average and the maximal length of aframe-header of a data packet is minimized.

A second advantage of the method of the present invention is that thespectral efficiency of a communication system is maximized.

A further advantage of the method of the present invention is that thecorrection capability of the generated channel code is improved.

A further advantage of the method of the present invention is that thecomputational complexity of a decoder is decreased.

A further advantage of the method of the present invention is topossibly also obtain a fixed length for all code-words in order to havea same complexity of the maximum likelihood decoder of a variable lengthcode.

The method and the system for generating channel codes in particular fora frame-header described herein by way of example may be subject to manypossible variations without departing from the novelty spirit of theinventive idea; it is also clear that in the practical implementation ofthe invention the illustrated details may have different shapes or bereplaced with other technically equivalent elements.

For example, the method and the system for generating channel codes inparticular for a frame-header can be applied in any communication systemin which it is possible to vary the spectral efficiency and/or incommunication systems that do not use an ACM scheme.

It can therefore be easily understood that the present invention is notlimited to a method and a system for generating channel codes inparticular for a frame-header, but may be subject to many modifications,improvements or replacements of equivalent parts and elements withoutdeparting from the inventive idea, as clearly specified in the followingclaims.

1. A method for generating a channel code, in particular for aframe-header, wherein at least a code-word of said channel code isobtained by means of at least a concatenation of code-words of twoconstituent codes and said concatenation is performed on subsets ofcode-words of a first constituent code, having maximum length, withcode-words of a second constituent code.
 2. The method according toclaim 1, wherein said at least a code-word of said constituent codes isencapsulated into a code-word of said channel code.
 3. The methodaccording to claim 1, wherein said at least a code-word of said channelcode has length greater than remaining code-words of said channel code.4. The method according to claim 1, wherein a minimum distance of saidconstituent codes depends on signal to noise ratios, which areassociated with user types and they are different from each said usertype, and depends on a detection error rate.
 5. The method according toclaim 1, wherein said minimum distance is computed for each said usertype.
 6. The method according to claim 1, wherein a maximum code-wordlength of said channel code is obtained by the formula N_(i)=Σ_(m=1)^(M) n_(m) where M is the maximum number of said code-words of saidchannel code corresponding to an highest said signal to noise ratio, mis an integer index varying from one to M and is a length of saidcode-word of said constituent code at index m.
 7. The method accordingto claim 1, wherein said code-words of said channel code have variablelength.
 8. The method according to claim 1, wherein said constituentcodes are known linear codes.
 9. The method according to claim 1,wherein said at least a code-word of said channel code comprises asequence of bits having values equal to zero to equalize the length ofsaid at least a code-word.
 10. The method according to claim 9, whereinsaid code-words of said channel code have fixed length.
 11. The methodaccording to claim 1, wherein said channel code is an UEP code.
 12. Themethod according to claim 1, wherein said channel codes are suitable tobe used for a frame-header of a data packet of a communication system.13. A system for generating channel codes, comprising at least anencoder for carrying out the method according to claim
 1. 14. The systemaccording to claim 13, wherein said encoder is suitable to be used in acommunication system.
 15. The system according to claim 14, wherein saidcommunication system comprises a plurality of user types havingdifferent signal to noise ratios.
 16. The system according to claim 14,wherein said communication system is an ACM (“Adaptive Coding andModulation”) communication system.